Representing Spatial Trajectories as Distributions
Representing Spatial Trajectories as Distributions
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DOI：
10.48550/arxiv.2210.01322
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发表时间：
2022-10
期刊：
影响因子：
--
通讯作者：
D'idac Sur'is;Carl Vondrick
中科院分区：
文献类型：
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作者：
D'idac Sur'is;Carl Vondrick
我们引入了空间轨迹的表征学习框架。我们将轨迹的部分观测表示为学习潜空间中的概率分布，它表征了轨迹未观测部分的不确定性。我们的框架允许我们从任何连续时间点的轨迹中获得样本，包括内插和外推。我们灵活的方法支持直接修改轨迹的特定属性，例如其速度，以及将不同的部分观测组合成单个表示。实验表明，该方法在预测任务中优于基线。
We introduce a representation learning framework for spatial trajectories. We represent partial observations of trajectories as probability distributions in a learned latent space, which characterize the uncertainty about unobserved parts of the trajectory. Our framework allows us to obtain samples from a trajectory for any continuous point in time, both interpolating and extrapolating. Our flexible approach supports directly modifying specific attributes of a trajectory, such as its pace, as well as combining different partial observations into single representations. Experiments show our method's advantage over baselines in prediction tasks.